基于Matlab的火灾探测报警系统可靠度Monte Carlo仿真

介绍了火灾探测报警系统的结构,指出了火灾探测报警系统失效原因,针对火灾探测报警系统的特点,确定了蒙特卡洛可靠度分析法.给出了系统的概率模型,确定了在Matlab下应用Monte Carlo方法对火灾探测报警系统的可靠度进行仿真的具体步骤,得到火灾探测报警系统可靠度仿真曲线.将仿真结果与试验结果相比较,基本一致,验证了该仿真方法能正确描述火灾探测报警系统的可靠度随使用寿命的变化规律,为进一步应用该方法来分析火灾探测报警系统的的各可靠性指标提供了理论基础.     

高含硫气田试气与地面工程建设交叉作业安全管理方法初探

普光气田作为高含硫气田在国内大规模开发,尚属首例,在气田开发过程中没有可借鉴的经验.高含硫气井试气作业中易发生井喷、硫化氢泄漏等事故.普光气田地面工程建设施工难度大、地形复杂,按照工程进度要求,试气作业和地面集输工程施工两项作业交叉时必须实现“安全作业、安全施工”,这是普光气田开发的基本目标.通过制订安全管理制度、明确责任主体、服从业主统一管理等有效的安全管理方法,保证了目标的实现,获得安全管理经验,对今后高含硫气田大规模开发具有指导意义.     

加强危险场所电气安全检测,提高化工企业本质安全水平

针对近年来化工企业电气安全、防静电等事故多发的现状,本文通过对化工企业电气安全、防静电等安全设施存在的问题进行分析,阐述了化工企业电气安全、防静电设施存在的主要缺陷,强调了利用技术支撑手段对在用安全设施进行检测检验对提高企业本质安全水平的重要性,并对如何做好电气安全、防静电等安全设施的检测工作提出了建议。     

基于PBPK模型评价三氯乙烯的职业暴露健康风险

三氯乙烯(TCE)作为脱脂和清洗剂被广泛应用于五金、电镀和电子等行业。TCE的职业暴露会产生一系列健康风险,包括过敏症和致癌等。2012年TCE被美国环保局(US EPA)和国际癌症研究机构列为1类致癌物。采用吸附管采样-热脱附/气相色谱-质谱法分析了大连市某企业车间生产工况下空气中TCE浓度。基于生理学的药代动力学(PBPK)模型预测了呼吸暴露途径下TCE在职业工人体内组织中的动态分布、代谢产物生成情况和致癌风险。TCE在不同组织中预测的最大浓度呈现出脂肪肠充分灌注室支气管非充分灌注室肝脏静脉血动脉血的趋势。预测的与致癌有关的代谢产物最大浓度表现为三氯乙酸二氯乙酸三氯乙醛S-二氯乙烯基-L-半胱氨酸。在监测的TCE水平(39.2±24.4)μg·m-3下,暴露8 h·d-1,连续暴露20年,基于外暴露评价的职业工人致癌风险均值为1.31×10-5,该暴露水平下,基于PBPK模型预测的TCE内暴露与外暴露计算的致癌风险水平相近,但基于具有致癌性主要代谢产物的内暴露致癌风险值是外暴露风险值的1.17～1.73倍。TCE的暴露水平越高,基于内暴露方法和外暴露方法的致癌风险评价结果差异越大。敏感性分析表明,心输出血流量和充分灌注室血流量对PBPK模型输出结果具有重要影响。不确定性分析表明,模型参数变化会显著地影响PBPK模型输出结果,但变异在可接受水平。本研究结果说明,评价TCE暴露对人的致癌风险需要考虑其在体内的分布和代谢过程。     

Performance-based earthquake evaluation of a full-scale petrochemical piping system

Assessment of seismic vulnerability of industrial petrochemical and oil & gas piping systems can be performed, beyond analytical tools, through experimental testing as well. Along this line, this paper describes an experimental test campaign carried out on a full-scale piping system in order to assess its seismic behaviour. In particular, a typical industrial piping system, containing several critical components, such as elbows, a bolted flange joint and a Tee joint, was tested under different levels of realistic earthquake loading. They corresponded to serviceability and ultimate limit states for support structures as suggested by modern performance-based earthquake engineering standards. The so called hybrid simulation techniques namely, pseudo-dynamic and real time testing with dynamic substructuring, were adopted to perform seismic tests. Experimental results displayed a favourable performance of the piping system and its components; they remained below their yielding, allowable stress and allowable strain limits without any leakage even at the Near Collapse Limit State condition for the support structure. Moreover, the favourable comparison between experimental and numerical results, proved the validity of the proposed hybrid techniques alternative to shaking table tests.     

松散煤体渗流传热试验装置研制

为研究松散煤体低温氧化及通风供氧下的渗流传热规律,研发了渗流传热试验装置。试验装置由气体加湿系统、气体加热系统、煤样反应系统、数据采集系统和安全保护系统组成;本装置可实现自动加湿、控温、自动采集数据及处理数据。为解决装置使用和运行中的安全问题,设计了断电保护、有害气体检测等措施,保障试验装置的安全、可靠。     

基坑放坡开挖对下方既有地铁隧道影响计算及风险预测

为研究基坑放坡开挖对下方既有地铁隧道的影响以及预测隧道结构的风险,通过改进的计算方法得到放坡开挖基坑引起下方既有地铁隧道的竖向和横向附加荷载、位移、相对变形曲率共6个隧道结构安全的物理表现因子;将位移计算结果与前人理论计算结果、实测数据对比验证,并分析各土层物理力学参数对6个因子的敏感性;最后,基于正态分布概率模型对较敏感的土层物理力学参数随机取值,利用蒙特卡罗方法计算6个因子各级风险发生的概率和竖向、横向2类因子综合影响下隧道结构各级风险发生的总概率。研究结果表明:与原来仅限于矩形开挖基坑的计算方法相比,改进后的计算方法适用范围更广、实用性更强;在算例二分析中,隧道竖向位移和相对变形曲率超过控制值的概率分别为12%和68.7%,其余因子均为0,隧道竖向相对变形曲率是隧道结构处于不安全状态的最主要因子;若不采取预防措施,隧道结构将有高达73.27%的概率处于不安全状态,其中有68.7%的概率处于很不安全状态。     

RBF神经网络可靠度分析方法在边坡稳定性研究中的应用

针对边坡稳定性可靠度分析,当状态函数无法显式表达且传统计算方法求解复杂问题困难时,提出一种基于ABAQUS和粒子群优化径向基函数神经网络的可靠度分析方法。基于ABAQUS的强度折减方法计算所选随机变量对应的安全系数,利用径向基函数神经网络的数据拟合功能,建立模型并映射出安全系数和随机变量之间的关系,构造响应面功能函数;利用蒙特卡罗生成的大量随机样本代入功能函数得到相应的安全系数,进而计算边坡的失稳概率和可靠度指标来反映边坡稳定性。研究结果表明:相对于传统方法,本文方法计算效率更高、误差更小,适合实际工程应用。     

Development of a probabilistic blood lead prediction model

A partially probabilistic blood lead prediction model has been developed, based on the US Environmental Protection Agency integrated exposure-uptake-biokinetic blood lead model (IEUBK model). This study translated the IEUBK model into a spreadsheet format. The uptake submodel incorporates uncertainty distributions for exposure and bioavailability parameters. The biokinetic submodel is duplicated with a table incorporating partitioning and decay of lead levels in the body. As a case study, the probabilistic model is applied to a lead exposure scenario involving a former smelter site in Sandy, Utah. The probabilistic model produces less biased estimates of means and standard deviations than the deterministic model. Parameter uncertainty is propagated in the model by the use of Monte Carlo simulation. Thus, sensitivity analysis is possible, and driving variables can be determined.     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.